The most related work to ours is LMNet [1]}, which also separates moving and non-moving objects using LiDAR scans.
Instead of designing a new network structure, it reuses the off-the-shelf LiDAR semantic segmentation methods [2]}, [3]}, [4]}.
To obtain inter-frame motion information, it feeds the multi-frame residual images directly into the existing structure as extra channels to the range image.
Such simple concatenation without special design often can not accurately exploit the motion information contained in the spatio-temporal scan sequence.
Moreover, LMNet only uses range image representation, bringing many artifacts during back-projection to point clouds, as shown in Fig. REF . Different from LMNet, we propose a novel network. It uses two specific branches to extract appearance features from range images and temporal motion features from residual images, respectively.
And then, it uses motion-guided attention modules with different scales to fuse them.
In the final stage of decoding, we back-project the 2D features to the 3D point cloud, and use a lightweight
sparse convolution module to refine the segmentation results.
